Autoencoders have been used for feature matching in the past.
A siamese network was used by researchers to match the latent space representation of tops and bottoms from the FashionVC and MbFashion [1]}.
They used the outfits the fashionistas published on different social networks as their ground truths.
Another team used a convolutional autoencoder to match image patches between two infrared images for 3D reconstruction [2]}.
Their results indicate that their autoencoder was better for matching the features of the test part of their Multi-View Stereo InfraRed (MVSIR) imagery dataset compared to SIFT [3]}, deep convolutional feature point descriptors [4]}, and stereo matching convolutional neural network [5]}.
They mentioned that if the convolutional autoencoder is able to obtain good restoration quality then it has learned to extract the most salient information from the data.
Thus the hidden representations can be used for sparse image matching.
Another group used a convolutional autoencoder to learn features from images of finger veins and then used a support vector machine (SVM) to classify them [6]}.
They credited the convolutional autoencoder for effective learning of finger vein features, preserving the main information of the image, reducing redundant information, and improving the recognition efficiency with the help of their SVM classifier.
We train our autoencoder to match skin features in general.
